Agent skill

Rabbit Round

by stella in stella/stella

Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots.

Apache-2.0Auto-check passedDevelopment

Install Rabbit Round

skills CLI
$ npx skills add stella/stella --skill rabbit-round -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install stella/stella rabbit-round --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/stella/stella.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/rabbit-round .claude/skills/rabbit-round && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rabbit-round
GitHub stars
258
Token cost
~1.6k tokens
SKILL.md length
883 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots.

  • Works in 5 steps: Capture the Review State → Triage Every Actionable Bot Finding → Implement Before Replying → …
  • Tasks that involve Pull requests
  • SKILL.md covers 1. Capture the Review State, 2. Triage Every Actionable Bot…, 3. Implement Before Replying and 4. Reply With Verifiable…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Rabbit Round is an agent skill from stella/stella. Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Pull requests. The repository describes itself as: Open-source legal workspace. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Pull requests

Example prompts

  • “/rabbit-round”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Capture the Review State
  2. Triage Every Actionable Bot Finding
  3. Implement Before Replying
  4. Reply With Verifiable Evidence
  5. Recheck the Current Head

What it can do on your machine

Read from SKILL.md and the folder at commit 269655d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rabbit Round loads about 1.6k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 883 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from stella/stella at commit 269655d, republished under its Apache-2.0 licence (© stella). 883 words, ~1,561 tokens.

Download SKILL.mdSave it as .claude/skills/rabbit-round/SKILL.md (or your agent's skills folder).
name
rabbit-round
description
Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots.

Rabbit Round

Process one round of automated review feedback. Use /finish-pr when the user wants repeated monitoring until a pull request converges. Never request an automated review; handle the threads that arrive on their own.

1. Capture the Review State

Resolve the repository, PR, current head SHA, requester identity, draft state, and applicable comment-attribution rules. Fail visibly if the PR cannot be identified.

Pin every GitHub query and mutation to the resolved full owner/name repository and PR number; never rely on the checkout's implicit repository or branch. Require each fetched PR's repository identity, number, and headRefOid to match the captured identity. Before replying to or resolving feedback, refetch that exact PR and stop if its head changed; results from one head never authorize a mutation on another.

Fetch paginated review threads through GitHub GraphQL so unresolved state and thread replies are preserved. Fetch top-level issue comments separately. Record every participant and reply author in a thread, which comments apply to the current head, and which are stale. Classify participants from a fresh fetch. Record a receipt for every workflow reply and retain those receipts across resume or handoff. A review-thread reply receipt contains the returned review-comment node ID and exact content; a top-level reply receipt contains the returned issue-comment node ID and exact content. On later fetches, exclude a reply only when its surface, node ID, and content exactly match the corresponding receipt; never infer an exclusion from the requester account or an attribution footer. All remaining participants must be confirmed allowed bots; a human, mixed, or uncertain thread follows the human-thread rules.

Do not rely only on the REST review-comments list: it does not represent thread resolution or the complete conversation reliably.

2. Triage Every Actionable Bot Finding

Classify each unresolved bot review thread and each actionable top-level bot comment:

  • Accept: correct and improves safety, behavior, tests, or maintainability.
  • Accept with adjustment: the concern is valid but the proposed fix conflicts with repository structure or a stronger invariant.
  • Already addressed: current code or a pushed commit demonstrably resolves it.
  • Push back: incorrect, stale, speculative, or contrary to documented constraints.
  • Defer: accepted, but landing in a named follow-up PR because the enclosing workflow's review budget is spent. Only with the follow-up PR's URL, and every defer in one run names the same PR. Never for a verified release-blocking defect (security, authorization, data loss, corruption): those are fixed on the current head.

Read the cited code and applicable instructions before deciding. Treat security, authorization, data loss, and compatibility claims as hypotheses to verify, not as votes to accept automatically. Never modify or resolve human review threads.

3. Implement Before Replying

Apply accepted changes, including tests when they cover a real failure mode. Run focused checks while iterating and the repository's canonical affected-change or CI-equivalent verification before publication when practical.

Commit and push the implementation before saying it is fixed. Push a new branch normally; use --force-with-lease only after intentionally rebasing a published branch. Capture the resulting head SHA. A newly published head cannot be clean in the same pass, even when GitHub has not registered checks or reviewers yet; classify it using the Section 5 precedence.

Show full SKILL.md (358 more words)Show less

4. Reply With Verifiable Evidence

Reply in the review thread or top-level issue conversation for each handled bot finding. Keep responses short and factual:

  • implemented in <sha> with the relevant behavior
  • implemented with an adjustment and why
  • already addressed, with the code or commit that proves it
  • not changing, with a concrete repository constraint or technical reason
  • deferred to a named follow-up PR, with its URL

Follow repository attribution rules for GitHub comments. Do not claim a check passed unless it ran successfully on the reported head.

After replying, refetch each candidate thread before resolving it. Exclude only exact workflow reply receipts, then require every remaining participant to be a confirmed allowed bot. Triage any new bot finding before resolving; any human, unknown, mixed-participant, or uncertain arrival leaves the thread open. Resolve only when the finding is implemented, already addressed, answered with supported pushback, or deferred to a named follow-up PR. Top-level comments have no thread-resolution state: a reply naming the follow-up PR is the whole disposition there. Do not minimize bot summaries by default.

5. Recheck the Current Head

Refresh the PR after the push and report one status. Apply this precedence: failing_ci > needs_changes > pending_bots > clean.

  • failing_ci: a current-head required check is known to have failed, regardless of pending reviewers, actionable feedback, or a push in this round
  • needs_changes: no required check is known to have failed, but actionable automated feedback remains
  • pending_bots: no required check is known to have failed and no actionable automated feedback remains, but this round pushed the current head or a current-head automated review or required check is still running
  • clean: all current-head automated reviewers are terminal, required checks are green, and no actionable automated finding remains in a review thread or top-level comment. A top-level finding answered with a defer reply carrying the follow-up PR's URL is no longer actionable on later rounds, unless it names a verified release-blocking defect: no defer makes one of those non-actionable, and the status stays needs_changes until it is fixed on the current head

Preserve the PR's explicit draft state. This skill performs one pass; it does not schedule polling, merge, deploy, or bypass protections.

© stella, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/rabbit-round of stella/stella.

Open the folder on GitHubat commit 269655d

Compare with similar skills

Rabbit Round next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Rabbit Round compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Rabbit Round this skillstella/stella258—~1.6kAutomated safety check: PassApache-2.0
Finishing a Development Branchobra/superpowers296k5 repos~1.9kAutomated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Check PRonyx-dot-app/onyx32k2 repos~2.3kAutomated safety check: PassMIT
Understand Diff AnalysisEgonex-AI/Understand-Anything86k1 repos~1.4kAutomated safety check: PassMIT
PR Design DocOpenHands/OpenHands90k—~2.4kAutomated safety check: PassMIT

Similar skills

  • Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.

    296k GitHub starsUsed in 5 repos~1.9k tokens
    DevelopmentAuto-check passed
  • PR Babysitter

    openinterpreter/openinterpreter

    Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.

    69k GitHub starsUsed in 3 repos~4.2k tokens
    DevelopmentAuto-check passed
  • Check PR

    onyx-dot-app/onyx

    Checks a GitHub, GitLab, or Perforce (p4) pull request (or merge request, or shelved changelist) for unresolved review comments, failing status checks, and incomplete PR descriptions.

    32k GitHub starsUsed in 2 repos~2.3k tokens
    DevelopmentAuto-check passed
  • Understand Diff Analysis

    Egonex-AI/Understand-Anything

    Reads your git changes or a pull request against a prebuilt knowledge graph of the project to explain what changed, which components are affected and what is risky.

    86k GitHub starsUsed in 1 repo~1.4k tokens
    DevelopmentAuto-check passed
  • PR Design Doc

    OpenHands/OpenHands

    For a non-trivial pull request, write a self-contained HTML design doc under the temporary .pr/ directory and link a visibility-appropriate preview in the PR description, so maintainers grasp the…

    90k GitHub stars~2.4k tokensUpdated today
    DevelopmentAuto-check passed
  • WooCommerce Code Review

    woocommerce/woocommerce

    Reviews WooCommerce code changes against the project's standards, flagging backend PHP architecture, naming, documentation, data integrity and testing violations.

    11k GitHub starsUsed in 3 repos~1.1k tokens
    DevelopmentAuto-check passed

More from stella/stella

All 24 skills in this repo
  • Plan

    stella/stella

    Create a concise, evidence-backed implementation plan in the repository planning area when the user explicitly asks for a plan.

    258 GitHub stars~917 tokensUpdated today
    Auto-check passed
  • Answer From Sources

    stella/stella

    Answers data-protection (GDPR) questions grounded in the regulation and supervisory guidance, with a citation for every claim.

    258 GitHub stars~735 tokensUpdated today
    Auto-check passed
  • Check Against Rules

    stella/stella

    Reviews a non-disclosure agreement against the firm's NDA checklist and reports findings with citations.

    258 GitHub stars~856 tokensUpdated today
    Auto-check passed
  • Intake To Draft

    stella/stella

    Collects the facts of an unpaid invoice, then drafts a payment demand letter.

    258 GitHub stars~537 tokensUpdated today
    Auto-check passed
  • Conventions Perf

    stella/stella

    Apply when a performance-guard check (network baseline, bundle baseline, DB query count, loader-prefetch lint, RC bailouts) fails or when touching a hot route/endpoint.

    258 GitHub stars~2.9k tokensUpdated today
    Auto-check passed
  • Apply when writing or reviewing React effects in apps/web. An agent skill from stella/stella.

    258 GitHub stars~2.8k tokensUpdated today
    Auto-check passed

Categories

Questions about Rabbit Round

What does Rabbit Round do?

Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots. Rabbit Round is an agent skill from stella/stella. Process one evidence-backed round of automated pull-request review comments from CodeRabbit, Gemini, Copilot, Devin, Greptile, and similar bots.

When should I use Rabbit Round?

Rabbit Round fits situations like: tasks that involve Pull requests.

How do I install Rabbit Round in Claude Code?

Run `npx skills add stella/stella --skill rabbit-round -a claude-code`. Or copy the skill folder (.agents/skills/rabbit-round in stella/stella) into .claude/skills/rabbit-round in your project. Claude Code loads it when a task matches its description.

How do I install Rabbit Round in Codex?

Run `npx skills add stella/stella --skill rabbit-round -a codex`. Or copy the skill folder (.agents/skills/rabbit-round in stella/stella) into .agents/skills/rabbit-round in your project. Codex loads it when a task matches its description.

Can I use Rabbit Round in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add stella/stella --skill rabbit-round -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rabbit-round, .gemini/skills/rabbit-round, .github/skills/rabbit-round and .opencode/skills/rabbit-round in your project.

What does Rabbit Round need to run?

SKILL.md names no scripts, command-line tools or credentials: Rabbit Round is instructions for the agent only.

Does Rabbit Round access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Rabbit Round safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rabbit Round use?

Rabbit Round is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rabbit Round use?

About 1.6k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rabbit Round?

Skills that share tags, products or a category with Rabbit Round: Finishing a Development Branch (obra/superpowers, 296k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars), Check PR (onyx-dot-app/onyx, 32k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 86k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rabbit Round?

stella (a GitHub organization) maintains it in stella/stella, which has 258 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 8, 2026.

Source: stella/stella on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.